Functional regression for data fusion and indirect measurements of physiological variables collected by wearable sensor systems and indirect calorimetry

Abstract

The paper describes application of different types of functional regression for analysis and modeling of the data collected by wearable sensor systems. The data have been recorded from human subjects while they were staying in whole room calorimeter chamber for 48 hours. This allowed very accurate measurements of their oxygen consumption, energy expenditure and substrate oxidation. These physiological parameters are notorious for their inaccuracy when measured in field conditions. The subjects wore two types of body sensors: the Hidalgo Equivital™ (Cambridge, UK) physiological monitors with a telemetry thermometer pill and iPro Professional Continuous Glucose Monitoring System (CGMS) (Medtronic MiniMed, Inc, Northridge, CA). The data collected by these two systems and by the calorimeter chamber were subsequently analyzed off-line using the functional regression techniques. The energy expenditure, substrate oxidation, and body core temperature were used as response variables, while heart rate, respiratory rate, subcutaneous glucose concentration, and skin temperature were used as predictors. The results show that the 24-hours and instantaneous energy expenditure values can be inferred from instantaneous measurements of heart rate, respiratory rate and glucose concentrations. Also, the body core temperature can be inferred from heart rate, respiratory rate, glucose concentration, and skin temperature. The substrate oxidation was the most difficult parameter to infer and it can only be accomplished during the exercise activity.